code: def create_dataset(self) -> Dataset:
      if self.dataset_size not in ["250K", "1M", "10M", "270M"]:
          raise ValueError(
              "Only '250K', '1M', '10M', and '270M' are supported for dataset_size."
          )
      if self.dataset_dimension != "2D":
          raise ValueError(
              "Currently, only '2D' is supported for dataset_dimension."
          )
      if self.dataset_size == "270M":
          answer = ""
          while answer not in ["y", "n"]:
              answer = input(
                  """You're about to download 270M SMILES strings.
                  This dataset is 23GB. Are you sure you want to continue? (Y/N)"""
              ).lower()
          if answer == "n":
              raise ValueError("Choose a smaller dataset_size.")
      filename = self.name + ".csv"
      dataset_file = os.path.join(self.data_dir, filename)
      if not os.path.exists(dataset_file):
          compressed_file = self.name + ".tar.gz"
          if not os.path.exists(compressed_file):
              dc.utils.download_url(
                  url=ZINC15_URL + compressed_file,
                  dest_dir=self.data_dir,
              )
          dc.utils.untargz_file(
              os.path.join(self.data_dir, compressed_file),
              self.data_dir,
          )
      loader = dc.data.CSVLoader(
          tasks=self.tasks,
          feature_field="smiles",
          id_field="zinc_id",
          featurizer=self.featurizer,
      )
      return loader.create_dataset(dataset_file, shard_size=8192)
  
file_path: deepchem/deepchem/molnet/load_function/zinc15_datasets.py
imports: import os
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 2 for calling the _Zinc15Loader obj's underlying member method code with example usage: obj.create_dataset() and returns: loader